Agricultural research, crop management, and seed quality evaluation all depend much on plant seed identification. Often labor-intensive, traditional seed identification techniques call for specialist knowledge or individual experienced in the craft with experience. This paper presents a new plant seed image identification system meant to automate and improve seed categorization by combining deep learning with traditional machine learning methods. Digital seed pictures are processed by the system using Convolutional Neural Networks (CNNs) to extract high-level characteristics. Principal Component Analysis (PCA) is then used to hone these qualities, hence lowering dimensionality and removing duplication. The seeds are finally classified using a Logistic Regression (LR) model depending on the lower feature set. Capturing changes in size, shape, colour, and texture across several species, a thorough and varied collection of seed pictures trains and evaluates the system. Metrics including accuracy, precision, recall, and F1-score are used to evaluate performance. Experimental findings show that the suggested CNN-PCA-Logistic Regression pipeline delivers good classification accuracy while preserving computational economy. A comparison with conventional CNN classifiers emphasizes even more the efficacy of our hybrid technique for plant seed identification activities.